Optimal Power Allocation Algorithm Research Articles

DNN-based algorithm for joint SIC ordering and power allocation in downlink NOMA-enabled heterogeneous networks

In the heterogeneous network (HetNet) employing downlink non-orthogonal multiple access (NOMA), we focus on the non-convex optimization problem to optimize the spectral efficiency (SE) while the users satisfy the quality-of-service (QoS) requirement. In the previous work, the optimal joint successive interference cancellation and power allocation (JSPA) algorithm for maximizing SE is proposed to solve the mixed-integer non-linear programming (MINLP) problem in NOMA-enabled HetNet. However, the optimal solution requires exponential complexity by the number of base stations (BSs). Therefore, we present a deep neural network (DNN)-based algorithm for JSPA to reduce the complexity. In particular, to deal with the MINLP-based JSPA problem, we reformulate it into an equivalently simple problem that optimizes only the power consumption of BSs. Then, we introduce the unsupervised DNN-based method for JSPA to handle the simplified problem. The presented scheme yields improved SE and outage performance compared with traditional DNN-based methods. Additionally, we propose a user selection scheme with low complexity to enhance the SE of the proposed DNN-based power allocation. Through simulations, we illustrate that the suggested DNN-based scheme can attain SE performance similar to that of the optimal scheme.

Queue-Aware Resource Allocation in Full-Duplex Multi-Cellular Wireless Networks

In this paper, we aim to tackle the challenges of resource block scheduling and power allocation in the context of multi-cell full-duplex wireless networks. This is a more realistic setting than the single cell scenario, and it better envisions how full-duplex wireless communications could eventually be implemented. We propose an optimal queue-aware joint scheduling and power allocation algorithm for full-duplex wireless networks in a multi-cell scenario. Because of its mathematical intractability, we decouple the problem and solve it for scheduling first, and for power allocation second. We consider both indoor and outdoor scenarios and show that the gains of multi-cell full-duplex wireless networks, with respect to their half-duplex counter-parts, are not always prevalent. Furthermore, we highlight the importance of inter-cell cooperation when it comes to scheduling resources and show that depending on the scenario at hand, interference mitigation from inter-cell cooperation can improve the performance of user equipment in terms of throughput and waiting delay. Finally, we show that power allocation can improve user equipment throughput with its efficiency being tied to the deployment scenario at hand.

Self-Organized and Distributed Green Resource Allocation for Space–Air–Ground IoT Networks

To deal with the explosion connections and data volume for emergency communication or hot spot capacity enhancement with massive Internet of Things (IoT) devices, deploying aerial base stations (AeBS) on unmanned aerial vehicles (UAV) to generate heterogeneous space-air-ground networks is considered to be a quite effective method. However, the flying AeBSs and back-hauling to existing heterogeneous networks (HetNets) lead to network energy consumption a key point. To ensure the energy-efficient operation of space-air-ground networks for smart IoT applications, we put forward the cluster-based HetNets energy-efficient resource allocation mechanism (CHERA). The scheme first divides the entire network into multiple independent BS clusters with the K-means++ algorithm for distributed Energy efficiency (EE) optimization. Then we propose a greedy BS sleeping strategy and a Lagrangian-dual-based optimal power allocation algorithm for the maximization of EE in each BS cluster. The EE optimization of space-air-ground IoT networks is implemented under the SON framework to make sure of the efficient and reliable operation of the network. Simulation results indicate that energy consumption is effectively decreased with the mechanism. It boosts the EE of space-air-ground networks by 23.8% compared with a baseline algorithm in which BSs are all in active mode with no power optimization. The result is expected to be useful for achieving future green space-air-ground networks IoT applications.

Underwater optical wireless communication system: Deep learning CNN with NOMA-based performance analysis

This research is looking forward improving the performance for underwater optical wireless communication (UOWC) by applying a Non-orthogonal multiple access (NOMA) technique. We also get the benefit of the advantage the transmission based on convolutional neural network hybrid with a long short-term memory cell. The relays selection and power optimization are two main parameters to enhance the UOWC system performance. In this work, we suppose a pairing method for NOMA nodes. By replacing the inner dense connections with convolution layers, this model is proposed to overcome high complexity and over fitting to improve the model performance. The obtained performance for sum rates show that NOMA outperforms the orthogonal multiple access system by ~ 6%. Applying a step-by-step sub-optimization algorithm (SSOPA) yields better results than using fixed power allocation (FPA), while using a global optimal power allocation algorithm (GOPA) increases the sum rates over both FPA and SSOPA. It is found that the improvement when using GOPA combined with CNN approach enhances the performance of sum rates by ~ 2.5% than using the independent-relay-aided NOMA (ICNOMA) for UOWC. The GOPA improvement is 1.2%, 2.5%, 8.7% over FPA and is 0.12%, 0.34%, 2.09% over SSOPA, for clear, pure, and coastal water, respectively. The ICNOMA outperforms both ordinary NOMA (ONOMA) and cooperative NOMA (CNOMA) without independent relay nodes. The ICNOMA achieves an improvement over ONOMA and CNOMA by 20.4% and 3.2%, respectively.

Reliable Transmission for NOMA Systems With Randomly Deployed Receivers

Non-orthogonal multiple access (NOMA) is regarded as a promising technology in achieving high capacity and massive connectivity. In this paper, the reliable transmission scheme of downlink NOMA systems is investigated. In particular, we divide the disc covered by the base station into several annular areas, where the receivers are randomly located following a uniform distribution. In this way, NOMA pairing is performed by randomly selecting receivers from two different areas. Firstly, we derive the closed-form expressions of bit error rate (BER) with quadrature phase-shift keying (QPSK) modulation, where the channel is modeled as small-scale Rayleigh fading and large-scale path loss. To achieve reliable communications, then, the BER performance of the receiver with the worst channel gain in each area is studied. Finally, an optimal power allocation algorithm is proposed, which obtains the minimum transmission power and optimal power allocation factor with a given BER constraint of all receivers. Extensive simulations demonstrate the accuracy of obtained BER expressions and the effectiveness of the proposed algorithm. These results provide valuable insight into realizing on reliable transmission of NOMA with randomly deployed receivers.

SCMA-Based Multiaccess Edge Computing in IoT Systems: An Energy-Efficiency and Latency Tradeoff

Sparse code multiple access (SCMA) is a kind of code-domain non-orthogonal multiple access (NOMA) scheme, which can support the increasing requirements for high spectral efficiency and massive connections. Meanwhile, multi-access edge computing (MEC) is a promising technology for providing resource-constrained users with computing resources. In this paper, we propose a novel optimization scheme in SCMA-based MEC network from the perspective of energy and latency for the Internet of Things (IoT) devices. Specifically, a system utility is first used to calculate the weighted energy consumption and task execution latency. The initial utility minimization problem is non-convex and then can be subdivided into two tractable subproblems by fixing task offloading decisions, namely, optimal local computing via CPU frequency scheduling and optimal edge computing via SCMA codebook assignment, subcarrier power allocation, and MEC server computing resources distribution. Primarily, a joint SCMA codebook assignment based on the bidirectional matching principle and optimal power allocation algorithm is proposed. Moreover, we come up with CPU frequency scheduling strategies utilizing convex optimization to optimize the computing resources allocation of local devices and the MEC server. Finally, a low-complexity task offloading policy based on simulated annealing is presented. Numerical results show that our proposed joint optimization algorithm for resource allocation and task offloading can achieve a good compromise between time delay and energy consumption for IoT devices. It is demonstrated that the proposed strategy has a remarkable advantage compared to previous SCMA-MEC schemes.

Energy-Efficient Optimal Power Allocation for SWIPT Based IoT-Enabled Smart Meter.

This paper presents an internet of things (IoTs) enabled smart meter with energy-efficient simultaneous wireless information and power transfer (SWIPT) for the wireless powered smart grid communication network. The SWIPT technique with energy harvesting (EH) is an attractive solution for prolonging the battery life of ultra-low power devices. The motivation for energy efficiency (EE) maximization is to increase the efficient use of energy and improve the battery life of the IoT devices embedded in smart meter. In the system model, the smart meter is equipped with an IoT device, which implements the SWIPT technique in power splitting (PS) mode. This paper aims at the EE maximization and considers the orthogonal frequency division multiplexing distributed antenna system (OFDM-DAS) for the smart meters in the downlink with IoT enabled PS-SWIPT system. The EE maximization is a nonlinear and non-convex optimization problem. We propose an optimal power allocation algorithm for the non-convex EE maximization problem by the Lagrange method and proportional fairness to optimal power allocation among smart meters. The proposed algorithm shows a clear advantage, where total power consumption is considered in the EE maximization with energy constraints. Furthermore, EE vs. spectral efficiency (SE) tradeoff is investigated. The results of our algorithm reveal that EE improves with EH requirements.

Optimization of the quality‐to‐power ratio of scalable video code video transmission in millimeter‐wave massive multiple‐input multiple‐output systems

AbstractConsider a millimeter‐wave (mmWave) massive multiple‐input multiple‐output (MIMO) system, where a base station (BS) relying on discrete lens antenna (DLA) array transmits scalable video code (SVC) video to multiple single‐antenna vehicles. Taking into account both the video quality for vehicles and the communication resource consumption of BS, we define a new metric given by the quality of received video to the power consumption ratio (QPR) in order to measure the system efficiency. The objective of this article is to maximize the minimum QPR among vehicles by designing the antenna selection and power allocation scheme, while ensuring the constraints of total transmit power and DLA array structure. Due to the piecewise form of the video quality function, it is intractable to solve the formulated problem, which is therefore decomposed into antenna selection and power allocation subproblems. A maximum equivalent channel gain (MECG) algorithm and an iteration algorithm are proposed for antenna selection subproblem with different complexities, respectively; while an optimal power allocation (OPA) algorithm is designed by analyzing three cases of transmit power constraints. Simulation results indicate that the proposed MECG algorithm has moderate performance with low‐complexity, while the OPA algorithm can help to achieve the optimal QPR in contrast to other algorithms.

Performance Analysis of NOMA Downlink for Next- Generation 5G Network with Statistical Channel State Information

Non-orthogonal multiple access (NOMA) is a compelling strategy that helps wireless networks of the fifth-generation (5G) to fulfill the diverse demands of increased fairness, high reliability, extensive connectivity, low delay and superior performance. Traditionally, NOMA network presumes perfect transmitter-side channel state information (CSI), which is almost impossible for a number of communication scenarios. Furthermore, this paper contemplates the realistic NOMA downlink method in the Rayleigh fading networks, where the statistical CSI linked with each user is known to the transmitter. We evaluate the outage probability of the proposed model and obtain a closed-form framework of the probability of outage for each user. Besides, the source can optimize the network’s achievable sum-rate for different users based on statistical CSI. The precision of our outage probability analysis and the optimum power allocation algorithm proposed are both verified by the analytical and simulation results.

An optimized power allocation algorithm for cognitive radio NOMA communication

The primary objective of cognitive radio network is to effectively utilize the unused spectrum bands. In cognitive radio networks, spectrum sharing between primary and secondary users is accomplished using either underlay or interweave cognitive radio approach. Non orthogonal multiple access (NOMA) is the proven technology in the present wireless developments, which allows the coexistence of multiple users in the same orthogonal block. The new paradigm cognitive radio NOMA (CR-NOMA) is one of the potential solutions to fulfill the demands of future wireless communication. This paper emphasizes on practical implementation of NOMA in cognitive radio networks to enhance the spectral efficiency. The goal is to increase the throughput of the secondary users satisfying the quality of service (QOS) requirements of primary users. To achieve this, we have presented the optimized power allocation strategy for underlay downlink scenario to support the simultaneous transmission of primary and secondary users. Furthermore, we have proposed QOS based power allocation scheme for CR-NOMA interweave model to support the coexistence of multiple secondary networks. Also, the changes adopted in implementing superposition coding (SC) and successive interference cancellation (SIC) for CR-NOMA are highlighted. Finally, simulation results validate the mathematical expressions that are derived for power allocation coefficient and outage probability.

Energy-Efficient Power Allocation in Non-Linear Energy Harvesting Multiple Relay Systems

In this paper, to maximize the energy efficiency (EE) in the two-hop multi-relay cooperative decoding and forwarding (DF) system for simultaneous wireless information and power transmission (SWIPT), an optimal power allocation algorithm is proposed, in which the relay energy harvesting (EH) adopts a nonlinear model. Under the constraints, including energy causality, the minimum transmission quality of information and the total transmission power at the relays, an optimization problem is constructed to jointly optimize the transmit power and power-splitting (PS) ratios of multiple relays. Although this problem is a nonlinear fractional programming problem, an iterative algorithm is developed to obtain the optimal power allocation. In particular, the joint power allocation at multiple relays is first decoupled into a single relay power allocation, and then single-relay power allocation is performed by the Dinkelbach iteration algorithm, which can be proven that it is a convex programming problem. Its closed form solutions for different polylines of EH models are obtained by using mathematical methods, such as monotonicity, Lagrange multipliers, the KKT condition and the Cardan formula. The simulation results show the superiority of the power allocation algorithm proposed in this paper in terms of EE.

Adaptive Coordinated Direct and Relay Transmission for NOMA Networks: A Joint Downlink-Uplink Scheme

Coordinated direct and relay transmission (CDRT) is a promising strategy to extend cell coverage. However, higher spectral efficiency (SE) cannot be guaranteed in CDRT due to the cooperative process. Thus, non-orthogonal multiple access (NOMA) based CDRT is investigated, where a relaying user assists a base station (BS) in communicating with a cell-edge user (CEU) while the BS directly communicates with a cell-center user (CCU). Specifically, a novel joint downlink-uplink adaptive scheme is proposed for NOMA-based CDRT to improve the SE further. The proposed scheme jointly designs uplink and downlink via NOMA and adaptive transmission to implement bidirectional communication. First, we study the performance of the proposed scheme under both perfect and imperfect successive interference cancellation (SIC), and closed-form expressions are derived for the outage probability, diversity order, outage throughput (OT), ergodic sum rate (ESR), and energy efficiency (EE). Then, an optimal power allocation algorithm using the segment optimization and a modified particle swarm optimization algorithm are proposed to maximize the ESR under perfect and imperfect SIC, respectively. Numerical results verify the superiority of the proposed scheme in terms of the ESR, OT, EE, and the outage performance of CEU. The effectiveness of the proposed power allocation algorithms is also validated.

Training Deep Neural Network for Optimal Power Allocation in Islanded Microgrid Systems: A Distributed Learning-Based Approach.

Currently, numerical optimization methods are used to solve distributed optimal power allocation (OPA) problems for islanded microgrid (MG) systems. Most of them are developed based on rigorous mathematical derivation. However, the complexity of such optimization algorithms inevitably creates a gap between theoretical analysis and real-time implementation. In order to bridge such a gap, in this article we provide a new distributed learning-based framework to solve the real-time OPA problem. Specifically, inspired by the human-thinking scheme, distributed deep neural networks (DNNs) together with a dynamic average consensus algorithm are first employed to obtain an approximate OPA solution in a distributed manner. Then a distributed balance generation and demand algorithm is designed to fine-tune it to obtain the final optimal feasible solution. In addition, it is theoretically proved that the proposed DNN can well approximate one existing OPA algorithm (Guo et al. 2018), where quantitative numbers of at most how many hidden layers and neurons are provided. Several experimental case studies show that our proposed distributed learning framework can achieve similar optimal results to those obtained by using typical existing distributed numerical optimization methods while it is superior in terms of simplicity and real-time capability.

Energy-Efficient Decoupled Access Scheme for Cellular-Enabled UAV Communication Systems

This article proposes a decoupled access scheme with enhanced energy efficiency (EE) for cellular networks serving a mix of randomly distributed ground and areal users. While unmanned aerial vehicles (UAVs) benefit from line-of-sight (LOS) dominant channels with their serving base-stations (BSs), their omnidirectional antennas could cause high interference to ground users (GUEs). To overcome this, a downlink (DL) and uplink (UL) decoupled (DUDe) access scheme is proposed in which the UAVs’ control and nonpayload communication links are separated from the high-capacity data links by connecting to different BSs and transmitting over different frequency bands in the UL and DL directions. The GUE-BS channels are also decoupled in tandem to minimize interference. Moreover, as battery life is a major constraint for UL transmissions, an optimal low-complexity power allocation algorithm—based on fractional programming and successive convex approximation—is proposed for optimizing the EE of this DUDe scheme. Our numerical results demonstrate that the proposed scheme can achieve several times higher sum-rates in both UL and DL directions than its coupled benchmark counterparts. Moreover, it will be shown that in addition to improving the GUE sum-rate by 15%, the devised power allocation algorithm achieves twice higher GUE EE than conventional fractional power control.

Multi‐antenna joint covert communication with a public communication link over wireless fading channel

This paper studies multi-antenna covert communication exploiting a public communication link over the wireless fading channel. In particular, a relatively practical covert communication scenario is considered for the first time, where a warden with radiometer may improve its detection accuracy by jointly detecting uplink covert pilot and downlink covert communication. To this end, a joint covert communication system, jointly considering uplink covert pilot, imperfect channel state information issue and downlink covert communication, is proposed and further studied. Specifically, the detection performance of warden and the covertness performance of system are comprehensively studied, respectively. Afterwards, an optimization problem for covert capacity is formulated to maximize overall performance. Then, with our analysis and simplification, an optimal power allocation algorithm is designed for covert capacity accordingly. Our examination shows a win-win situation has been achieved in the proposed system, that is, covert communication would rarely affect the normal communication of public link and occupy its communication resources. Interestingly enough, we analytically find an invariance property of downlink covert communication that covertness performance is not impacted by noise levels or warden's position. Based on this extraordinary finding, we conclude that the downlink covert communication system can perfectly combat with a powerful warden adopting power trend test method, while the joint covert communication system may resist that through a pilot deception scheme.

Joint user clustering and salp based particle swarm optimization algorithm for power allocation in MIMO-NOMA

Non-Orthogonal Multiple Access (NOMA) provides a positive solution for multiple access issues and meets the criteria of fifth-generation (5G) networks by improving service quality that includes vast convergence and energy efficiency. The problem is formulated for maximizing the sum rate of MIMO-NOMA by assigning power to multiple layers of users. In order to overcome these problems, two distinct evolutionary algorithms are applied. In particular, the recently implemented Salp Swarm Algorithm (SSA) and the prominent Optimization of Particle Swarm (PSO) are utilized in this process. The MIMO-NOMA model optimizes the power allocation by layered transmission using the proposed Joint User Clustering and Salp Particle Swarm Optimization (PPSO) power allocation algorithm. Also, the closed-form expression is extracted from the current Channel State Information (CSI) on the transmitter side for the achievable sum rate. The efficiency of the proposed optimal power allocation algorithm is evaluated by the spectral efficiency, achievable rate, and energy efficiency of 120.8134bits/s/Hz, 98Mbps, and 22.35bits/Joule/Hz respectively. Numerical results have shown that the proposed PSO algorithm has improved performance than the state of art techniques in optimization. The outcomes on the numeric values indicate that the proposed PSO algorithm is capable of accurately improving the initial random solutions and converging to the optimum.

Spectral and energy efficient power allocation for MIMO broadcast channels with individual delay and QoS constraints

In this paper, we propose a novel power allocation algorithm to maximize the spectral efficiency (SE) and energy efficiency (EE) of multiple-input multiple-output (MIMO) broadcast channels under individual quality of service (QoS) constraints. In several wireless applications, one of the most important factors for QoS guarantee is delay outage. To address the impact of delay outage occurring in the link layer, effective capacity (EC) that means the maximum constant arrival rate satisfying statistical delay-QoS constraint is considered. Using a novel performance metric, effective-EE, which is defined as EC divided by total power consumption, we formulate an EC and effective-EE maximization problem with QoS constraints as an adaptive power allocation problem. By applying Lagrangian method, we solve optimization problem and propose an optimal power allocation algorithm. Simulation results demonstrate that our proposed algorithm can improve the EC and effective-EE performance.

Joint power and subcarrier allocation for SWIPT in cognitive relay system

In this paper, we study power and subcarrier allocation in decode-and-forward (DF) cognitive relay network based on orthogonal frequency division multiple access (OFDMA) technology, in which power splitting (PS) is used for the secondary users (SUs) supporting simultaneous wireless information and the power transfer (SWIPT). We attempt to maximize the energy efficiency (EE) of the secondary system subject to the constraints of minimum system rate, maximum transmission power and minimum harvested energy. The optimal power allocation (OPA) algorithm is proposed to determine the optimal solution of relay transmission power, power splitting ratio and subcarrier allocation. To tackle the original non-convex optimization problem, the Dinkelbach method is used for fractional programming and the problem is decomposed into two solvable sub-problems. The Lagrangian dual algorithm is utilized to determine the optimal transmission power and the power splitting ratio. The Hungarian algorithm is then introduced to determine the optimal subcarrier allocation. Compared with some existing algorithms, the simulation results show that the proposed OPA scheme is able to achieve higher energy efficiency when certain communication quality is required.

Joint relay selection and power allocation for NOMA‐based multicast cognitive radio networks

In this study, the problem of joint relay selection and power allocation (J-RS-PA) for NOMA-based multicast cognitive radio networks is considered. In particular, the aim is to simultaneously maximise the end-to-end SINR/SNR of the primary and secondary transmitter–receiver (TR) pairs, subject to quality-of-service (QoS) constraints. Communication between the primary and secondary TR pairs is performed over two-phases, namely, the broadcasting phase, and the cooperation phase. In the broadcasting phase, the primary and secondary transmitters broadcast their data symbols; while in the cooperation phase, the selected relay forwards the decoded symbols to their intended receivers. However, the formulated J-RS-PA problem happens to be non-convex, resulting in computationally-prohibitive complexity. Consequently, an optimal low-complexity two-stage relay selection and power allocation (TS-RS-PA) algorithm is devised, which is based on the solution of linear programming problem reformulations. Simulation results are presented to validate the proposed TS-RS-PA algorithm, which is shown to yield the optimal SINR/SNR values in comparison to the J-RS-PA scheme, but with lower computational complexity, while satisfying QoS constraints.

Power Allocation for Intelligent Interference Exploitation Aided Physical-Layer Security in OFDM-Based Heterogeneous Cellular Networks

In this paper, we consider an orthogonal frequency division multiplexing (OFDM) based heterogeneous cellular network consisting of a macro cell and a small cell, where a macro-cell base station (MBS) and a small-cell base station (SBS) communicate with respective macro-cell user (MU) and small-cell user (SU) with the existence of a common eavesdropper across different OFDM subcarriers. In order to make full use of the mutual interference between MBS and SBS against eavesdropping, an artificially designed signal is emitted at MBS to cancel out the interference received at MU as well as to interfere with the common eavesdropper at each subcarriers. For the purpose of further improving the secrecy performance, a sum secrecy rate (SSR) of OFDM-based heterogeneous cellular networks is maximized by optimizing the power allocation between MBS and SBS across different OFDM subcarriers when the eavesdropper owns the global instantaneous channel state information (ICSI), thus called ICSI based SSR maximization (ICSI-SSRM). As for the case when the ICSI of wiretap channels is unknown, we propose a statistical CSI based SSR maximization (SCSI-SSRM) scheme, where the statistical characteristics of channels from MBS and SBS to eavesdropper are employed to optimally allocate powers of MBS and SBS across different subcarriers under the constraint of MBS's total transmit power. The formulated ICSI-SSRM and SCSI-SSRM problems are all non-convex due to form of the difference between two-convex (D.C.) functions. Thus, we utilize the D.C. approximation approach to respectively convert original optimization problems into convex problems. Moreover, iterative optimal power allocation algorithms for ICSI-SSRM and SCSI-SSRM schemes are also presented to obtain their respective SSR values. Simulation results illustrate that the ICSI-SSRM and SCSI-SSRM algorithms can converge to their optimal values, which confirms the correctness and validation of the proposed algorithms. In addition, numerical results are also given to show that the ICSI-SSRM and SCSI-SSRM schemes outperform conventional power allocation methods in terms of their SSR performance.